Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.
ROLE/RESPONSIBILITIES
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Perform rigorous and innovative research to discover systematic anomalies in global macro markets (futures, FX, etc.)
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Perform feature engineering with price-volume, order book and alternative data at intraday to daily horizons in mid frequency trading space
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Perform feature combination and monetization using various modeling techniques
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Manage the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation
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Maintain and improve portfolio trading in a production environment
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Contribute to the analysis framework for scalable research
REQUIREMENTS
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Background in mathematics, statistics, machine learning, computer science, engineering, quantitative finance, or economics
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2-6 years of signal research experience in macro trading as part of a trading team
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Specialization in swaps, fixed income, or commodities trading a plus.
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Prior professional experience with feature engineering, modeling, or monetization
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Ability to efficiently format and manipulate large, raw data sources
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Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas
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Strong command of foundations of applied and theoretical statistics, linear algebra, and machine learning techniques
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Collaborative mindset with strong independent research abilities